Description

This is a documentation bug. The parameter of API mxnet.test_utils.check_numeric_gradient is not consistent between signature and Parameter section. There is a parameter check_eps in the Parameter section, but it is not in the signature.

Link to document: https://mxnet.apache.org/versions/1.6/api/python/docs/api/mxnet/test_utils/index.html#mxnet.test_utils.check_numeric_gra

Bug Report

These tests were run on s390x. s390x is big-endian architecture.

Failure log for helper_test.py

________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________

self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>

    def test_make_tensor(self):  # type: () -> None
    

There are many links in Kinetics that have expired. As as result, everyone might not be using the same Kinetics dataset. As a reference, the statistics of the Kinetics dataset used in PySlowFast can be found here, https://github.com/facebookresearch/video-nonlocal-net/blob/master/DATASET.md. However, I cannot seem to find similar information for gluoncv. Will you guys be sharing the statistics and

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

Use case

Polyaxon tracking can be used to automatically log all information generated by Ludwig.

We should probably think about adding support for Polyaxon's tracking module in Ludwig contrib.

I have the same hardware envs, same network, but I could not get the result as you, almost half as you. Any best practices and experience? thanks very much! for bytePS with 1 instance and 8 GPU, I have similar testing result.

Description

While using tokenizers.create with the model and vocab file for a custom corpus, the code throws an error and is not able to generate the BERT vocab file

Error Message

ValueError: Mismatch vocabulary! All special tokens specified must be control tokens in the sentencepiece vocabulary.

To Reproduce

from gluonnlp.data import tokenizers
tokenizers.create('spm', model_p

Description

(A clear and concise description of what the feature is.)

• util.cumsum implementation https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/mx/util.py#L326 does not scale under mx.ndarray
• cumsum is 2-5 times slower than nd.cumsum under both mx.sym and mx.ndarray, and even fails for large 4-dim input

Sample test

Code

# import ...
def test_

Description

Add the dataset to the basic datasets.

References

• https://rajpurkar.github.io/SQuAD-explorer/